Investigating the Main Obstacles Faced by Indian Women in Academia with Worldwide Experience in Order to Advance the SDGs for Gender Equality and Decent Work
Bibliographic record
Abstract
This chapter explores the significant challenges women face in pursuing careers in the education sector, both domestically and internationally, with a focus on gender equality and decent work. Interviews with highly qualified women holding prominent academic positions provided valuable insights into their experiences. Qualitative analysis of the data revealed key obstacles, including inflexible employment systems, a lack of genderinclusive policies, and the persistence of traditional cultural norms. These challenges were evident across diverse regions, including India, the United States, the United Kingdom, Canada, France, the UAE, and Australia. Addressing issues of gender equality and decent work from an early stage is critical to enhancing women’s participation in education. 88 This is essential for fostering inclusive organizations and achieving sustainable social development. By examining cross-cultural perspectives, the chapter sheds light on strategies to overcome barriers and promote women’s engagement in the academic field. It emphasizes the importance of context in understanding and addressing gender inequality, highlighting the need for adaptable solutions that resonate with varying cultural and organizational frameworks. This work contributes to the broader dialogue on creating equitable opportunities for women in education, aligning with goals of inclusivity and sustainable progress.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".